420 research outputs found
Bioelectronic Sensor Nodes for Internet of Bodies
Energy-efficient sensing with Physically-secure communication for bio-sensors
on, around and within the Human Body is a major area of research today for
development of low-cost healthcare, enabling continuous monitoring and/or
secure, perpetual operation. These devices, when used as a network of nodes
form the Internet of Bodies (IoB), which poses certain challenges including
stringent resource constraints (power/area/computation/memory), simultaneous
sensing and communication, and security vulnerabilities as evidenced by the DHS
and FDA advisories. One other major challenge is to find an efficient on-body
energy harvesting method to support the sensing, communication, and security
sub-modules. Due to the limitations in the harvested amount of energy, we
require reduction of energy consumed per unit information, making the use of
in-sensor analytics/processing imperative. In this paper, we review the
challenges and opportunities in low-power sensing, processing and
communication, with possible powering modalities for future bio-sensor nodes.
Specifically, we analyze, compare and contrast (a) different sensing mechanisms
such as voltage/current domain vs time-domain, (b) low-power, secure
communication modalities including wireless techniques and human-body
communication, and (c) different powering techniques for both wearable devices
and implants.Comment: 30 pages, 5 Figures. This is a pre-print version of the article which
has been accepted for Publication in Volume 25 of the Annual Review of
Biomedical Engineering (2023). Only Personal Use is Permitte
An Energy-autonomous Wireless Sensor Network Development Platform
Internet-of-things enabled applications are increasingly popular and are expected to spread even more in the next few years. Energy efficiency is fundamental to support the widespread use of such systems. This paper presents a practical framework for the development and the evaluation of low-power Wireless Sensor Networks equipped with energy harvesting, aiming at energy-autonomous applications. An experimental case study demonstrates the capabilities of the solution
An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications
The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many applications, energy must be harvested from ultra-low power levels.
To tackle these stringent power constraints, this dissertation proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low input power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for WSNs is presented. In addition to conventional switching frequency control, clock nonoverlap time control is introduced to adaptively optimize the power conversion efficiency according to the sensed ambient power levels.
The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4ÎĽW input power, as opposed to a conventional continuous system which requires at least 18.7ÎĽW to maintain the same PCE. In this technique, the input power harvesting range is extended by 1.5x.
The technique is applied to a WSN implementation utilizing the IEEE 802.15.4- compatible GreenNet communications protocol for industrial and wearable applications. This allows the node to meet specifications and achieve energy autonomy when deployed in harsher environments where the input power is 49% lower than what is required for conventional operation
Vibration energy harvesters for wireless sensor networks for aircraft health monitoring
Traditional power supply for wireless sensor nodes is batteries. However, the
application of batteries in WSN has been limited due to their large size, low
capacity, limited working life, and replacement cost.
With rapid advancements in microelectronics, power consumption of WSN is
getting lower and hence the energy harvested from ambient may be sufficient to
power the tiny sensor nodes and eliminate batteries completely.
As vibration is the widespread ambient source that exists in abundance on an
aircraft, a WSN node system used for aircraft health monitoring powered by a
piezoelectric energy harvester was designed and manufactured.
Furthermore, simulations were performed to validate the design and evaluate
the performance.
In addition, the Z-Stack protocol was migrated to run on the system and initial
experiments were carried out to analyse the current consumption of the system.
A new approach for power management was reported, the execution of the
operations were determined by the amount of the energy stored on the
capacitor. A novel power saving interface was also developed to minimise the
power consumption during the voltage measurement
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